An Introduction to Computational Learning Theory

by Kearns, Michael J.; Vazirani, Umesh

• ISBN13: 9780262111935
• ISBN10: 0262111934
• Format: Hardcover
• Copyright: 1994-08-15
• Publisher: The MIT Press

Summary

Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Computational learning theory is a new and rapidly expanding area of research that examines formal models of induction with the goals of discovering the common methods underlying efficient learning algorithms and identifying the computational impediments to learning. Each topic in the book has been chosen to elucidate a general principle, which is explored in a precise formal setting. Intuition has been emphasized in the presentation to make the material accessible to the nontheoretician while still providing precise arguments for the specialist. This balance is the result of new proofs of established theorems, and new presentations of the standard proofs. The topics covered include the motivation, definitions, and fundamental results, both positive and negative, for the widely studied L. G. Valiant model of Probably Approximately Correct Learning; Occam's Razor, which formalizes a relationship between learning and data compression; the Vapnik-Chervonenkis dimension; the equivalence of weak and strong learning; efficient learning in the presence of noise by the method of statistical queries; relationships between learning and cryptography, and the resulting computational limitations on efficient learning; reducibility between learning problems; and algorithms for learning finite automata from active experimentation.

Author Biography

Michael J. Kearns is Professor of Computer and Information Science at the University of Pennsylvania.

Umesh Vazirani is Roger A. Strauch Professor in the Electrical Engineering and Computer Sciences Department at the University of California, Berkeley.

Table of Contents

Preface
The Probably Approximately Correct Learning Model	p. 1
Occam's Razor	p. 31
The Vapnik-Chervonenkis Dimension	p. 49
Weak and Strong Learning	p. 73
Learning in the Presence of Noise	p. 103
Inherent Unpredictability	p. 123
Reducibility in PAC Learning	p. 143
Learning Finite Automata by Experimentation	p. 155
Appendix: Some Tools for Probabilistic Analysis	p. 189
Bibliography	p. 193
Index	p. 205
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